Digitup Extension capture
A screen-recording flow designed for reporting without leaving the browser.
AI
Productivity
Screen recording
Bug reports
Tasks
Teams
ClipToTask: from screen recordings to actionable AI tasks
A productivity product that analyzes screen recordings and turns visual explanations into structured tasks, bug reports and product feedback for product, support and engineering teams.
Project overview
ClipToTask is designed to simplify how teams communicate bugs, improvements, incidents or visual feedback. Instead of writing long explanations, users can record their screen, explain what is happening and let the platform analyze that recording with artificial intelligence.
The system turns video content into actionable tasks, clear descriptions, potential bug reports and relevant clips that help the team understand the issue. The goal is to reduce friction between spotting an incident and turning it into useful work for product, engineering, QA or support.
Context and need
In many digital teams, feedback arrives in unstructured formats: long videos, Slack messages, emails, screenshots, loose notes or incomplete explanations. That creates wasted time, misunderstandings and unclear tasks.
ClipToTask was built to solve that: let anyone record what they see, explain it naturally and get structured output the team can use directly — without rewriting the full context by hand.
The problem to solve
The main challenge was turning a screen recording into truly useful information. Saving the video was not enough. The system had to interpret what was happening, detect context, extract key points and generate a clear task.
Speed also mattered. The tool had to work for teams that need to report bugs, review product, send feedback to engineering or document issues without spending time writing every detail manually.
The solution Digitup built
Digitup built a solution based on screen recording, content analysis and automatic task generation with AI. Users can start a recording from an extension, explain the problem or flow while navigating, and then the platform processes the content into structured output.
AI helps turn a visual explanation into actionable information: task title, description, steps to reproduce a bug, context, likely affected areas and relevant video fragments. That makes it easier for the team to understand the issue without reviewing the full recording manually.
What we built
Product layers designed to capture, interpret and deliver actionable work.
Content analysis
Processing video and spoken/written explanation to extract useful context.
Task generation
Structured output with title, description and actionable points for the team.
Bug reports
A format oriented to reproduce issues and support QA and engineering work.
Product feedback
Extraction of improvements and observations from real visual explanations.
Integration-ready base
Architecture prepared to connect with task-management tools later.
Key features
- Screen recording via browser extension
- Analysis of recorded content
- AI-driven context interpretation
- Automatic task generation
- Bug report creation
- Product feedback extraction
- Identification of relevant moments in the video
- Information organization for teams
- Flow designed for product, support, QA and engineering
- Room to evolve toward task-tool integrations
AI layer
Artificial intelligence is used to turn unstructured information into useful team content. The system can analyze the user’s explanation, detect intent, summarize the problem and generate a clearer task than a raw video or screenshot.
This layer reduces manual work and improves communication between non-technical people and technical teams — especially for bugs, product improvements or visual feedback.
Architecture and technology
The architecture is designed to capture visual information, process it and convert it into structured data. That allows the product to evolve toward integrations with task systems, internal tools, CRMs, support platforms or QA environments.
Browser extension
Multimodal AI
LLMs
Video processing
Custom backend
APIs
Task management
Automations
Where Digitup added value
We did more than wire an AI model: we designed the product flow, capture, processing and actionable output.
Product and flow
A fast path from recording to a usable task for the team.
Applied AI
Visual context interpretation and structured outputs with LLMs.
Backend and APIs
Processing, storage and a foundation for future team-tool integrations.
Project impact
ClipToTask helps shrink the time between spotting a problem and turning it into a clear task. It improves communication between product, support, engineering and QA by avoiding incomplete explanations or weak task descriptions.
The project shows how AI can apply to real workflows — not only as a chatbot, but as a layer that interprets visual information and turns it into concrete actions.
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